National spatial crop yield simulation using GIS-based crop production model
Identifieur interne : 000C32 ( Istex/Corpus ); précédent : 000C31; suivant : 000C33National spatial crop yield simulation using GIS-based crop production model
Auteurs : Satya Priya ; Ryosuke ShibasakiSource :
- Ecological Modelling [ 0304-3800 ] ; 2001.
English descriptors
- KwdEn :
Abstract
Traditional decision support systems based on crop simulation models are normally site-specific. In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model ‘Spatial EPIC’ was developed based on a crop simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is necessary to feed the fine resolution data at each grid-cell in order to ‘spatialize’ crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data, which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. national scale (approx. 50 km cell-size) and regional scale (approx. 10 km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account.
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DOI: 10.1016/S0304-3800(00)00364-1
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In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model ‘Spatial EPIC’ was developed based on a crop simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is necessary to feed the fine resolution data at each grid-cell in order to ‘spatialize’ crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data, which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. national scale (approx. 50 km cell-size) and regional scale (approx. 10 km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Satya Priya</name><affiliations><json:string>Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan</json:string><json:string>E-mail: satya@skl.iis.u-tokyo.ac.jp</json:string></affiliations></json:item><json:item><name>Ryosuke Shibasaki</name><affiliations><json:string>Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan</json:string><json:string>E-mail: satya@skl.iis.u-tokyo.ac.jp</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Crop simulation models</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Geographic Information Systems</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Agroecosystem</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>National analysis and planning</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Crop productivity</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>Full-length article</json:string></originalGenre><abstract>Traditional decision support systems based on crop simulation models are normally site-specific. In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model ‘Spatial EPIC’ was developed based on a crop simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is necessary to feed the fine resolution data at each grid-cell in order to ‘spatialize’ crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data, which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. national scale (approx. 50 km cell-size) and regional scale (approx. 10 km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account.</abstract><qualityIndicators><score>7.712</score><pdfVersion>1.2</pdfVersion><pdfPageSize>536 x 674 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>5</keywordCount><abstractCharCount>1587</abstractCharCount><pdfWordCount>5470</pdfWordCount><pdfCharCount>33533</pdfCharCount><pdfPageCount>17</pdfPageCount><abstractWordCount>226</abstractWordCount></qualityIndicators><title>National spatial crop yield simulation using GIS-based crop production model</title><pii><json:string>S0304-3800(00)00364-1</json:string></pii><genre><json:string>research-article</json:string></genre><host><volume>136</volume><pii><json:string>S0304-3800(00)X0111-1</json:string></pii><pages><last>129</last><first>113</first></pages><issn><json:string>0304-3800</json:string></issn><issue>2–3</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><title>Ecological Modelling</title><publicationDate>2001</publicationDate></host><categories><wos><json:string>science</json:string><json:string>ecology</json:string></wos><scienceMetrix><json:string>natural sciences</json:string><json:string>biology</json:string><json:string>ecology</json:string></scienceMetrix></categories><publicationDate>2001</publicationDate><copyrightDate>2001</copyrightDate><doi><json:string>10.1016/S0304-3800(00)00364-1</json:string></doi><id>F0529151583246462F9B64855CC85D97D06C54AC</id><score>0.042136</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/F0529151583246462F9B64855CC85D97D06C54AC/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/F0529151583246462F9B64855CC85D97D06C54AC/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/F0529151583246462F9B64855CC85D97D06C54AC/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">National spatial crop yield simulation using GIS-based crop production model</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>©2001 Elsevier Science B.V.</p></availability><date>2001</date></publicationStmt><notesStmt><note type="content">Fig. 1: Physical components of the model.</note><note type="content">Fig. 2: Modeling linkage diagram.</note><note type="content">Fig. 3: Brief schematic presentation of modeling under ‘Spatial-EPIC’.</note><note type="content">Fig. 4: Concept of ‘weather’ generator (generating high resolution temporal daily data from coarse resolution monthly data).</note><note type="content">Fig. 5: Concept of ‘slope’ generator (deriving the range and impact of two resolution DEM computed slope over soil loss).</note><note type="content">Fig. 6: Example of data applied in the model.</note><note type="content">Fig. 7: Characterization of fertilizer layer applied: (a) state level; (b) district level in the year 1990.</note><note type="content">Fig. 8: Characterization of simulated root zone soil moisture district level in the year 1990.</note><note type="content">Fig. 9: Main growing region of corn (maize), wheat and rice crop in India.</note><note type="content">Fig. 10: State wise comparison of maize crop yield (t/ha).</note><note type="content">Fig. 11: State wise comparison of wheat crop yield (t/ha).</note><note type="content">Fig. 12: State wise comparison of rice crop yield (t/ha).</note><note type="content">Fig. 13: Rough Spatial validation of maize crop in the year 1990–1991.</note><note type="content">Fig. 14: Rough Spatial validation of wheat crop in the year 1990–1991.</note><note type="content">Fig. 15: Rough Spatial validation of rice crop in the year 1990–1991.</note><note type="content">Fig. 16: Time-series validation of maize crop in Bihar.</note><note type="content">Fig. 17: Time-series validation of rice crop in Bihar.</note><note type="content">Fig. 18: Time-series validation of wheat crop in Bihar.</note><note type="content">Fig. 19: Sensitivity analysis of model response with two different resolutions of soil data over rice crop.</note><note type="content">Fig. 20: Direct impact of input data (soil pH) on crop yield; an example of model sensitivity.</note><note type="content">Fig. 21: Comparision of two different resolutions data impact over wheat yield.</note><note type="content">Fig. 22: Model application as a climate impact assessment over rice crop.</note><note type="content">Table 1: Means and standard deviations of reported and simulated yields</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">National spatial crop yield simulation using GIS-based crop production model</title><author xml:id="author-1"><persName><forename type="first">Satya</forename><surname>Priya</surname></persName><email>satya@skl.iis.u-tokyo.ac.jp</email><note type="correspondence"><p>Corresponding author. Tel.: +81-3-54526412; fax: +81-3-54526414</p></note><affiliation>Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan</affiliation></author><author xml:id="author-2"><persName><forename type="first">Ryosuke</forename><surname>Shibasaki</surname></persName><email>satya@skl.iis.u-tokyo.ac.jp</email><affiliation>Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan</affiliation></author></analytic><monogr><title level="j">Ecological Modelling</title><title level="j" type="abbrev">ECOMOD</title><idno type="pISSN">0304-3800</idno><idno type="PII">S0304-3800(00)X0111-1</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2001"></date><biblScope unit="volume">136</biblScope><biblScope unit="issue">2–3</biblScope><biblScope unit="page" from="113">113</biblScope><biblScope unit="page" to="129">129</biblScope></imprint></monogr><idno type="istex">F0529151583246462F9B64855CC85D97D06C54AC</idno><idno type="DOI">10.1016/S0304-3800(00)00364-1</idno><idno type="PII">S0304-3800(00)00364-1</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2001</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Traditional decision support systems based on crop simulation models are normally site-specific. In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model ‘Spatial EPIC’ was developed based on a crop simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is necessary to feed the fine resolution data at each grid-cell in order to ‘spatialize’ crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data, which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. national scale (approx. 50 km cell-size) and regional scale (approx. 10 km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>Crop simulation models</term></item><item><term>Geographic Information Systems</term></item><item><term>Agroecosystem</term></item><item><term>National analysis and planning</term></item><item><term>Crop productivity</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2000-06-06">Modified</change><change when="2001">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/F0529151583246462F9B64855CC85D97D06C54AC/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity><istex:entity SYSTEM="gr3" NDATA="IMAGE" name="gr3"></istex:entity><istex:entity SYSTEM="gr4" NDATA="IMAGE" name="gr4"></istex:entity><istex:entity SYSTEM="gr5" NDATA="IMAGE" name="gr5"></istex:entity><istex:entity SYSTEM="gr6" NDATA="IMAGE" name="gr6"></istex:entity><istex:entity SYSTEM="gr7" NDATA="IMAGE" name="gr7"></istex:entity><istex:entity SYSTEM="gr8" NDATA="IMAGE" name="gr8"></istex:entity><istex:entity SYSTEM="gr9" NDATA="IMAGE" name="gr9"></istex:entity><istex:entity SYSTEM="gr10" NDATA="IMAGE" name="gr10"></istex:entity><istex:entity SYSTEM="gr11" NDATA="IMAGE" name="gr11"></istex:entity><istex:entity SYSTEM="gr12" NDATA="IMAGE" name="gr12"></istex:entity><istex:entity SYSTEM="gr13" NDATA="IMAGE" name="gr13"></istex:entity><istex:entity SYSTEM="gr14" NDATA="IMAGE" name="gr14"></istex:entity><istex:entity SYSTEM="gr15" NDATA="IMAGE" name="gr15"></istex:entity><istex:entity SYSTEM="gr16" NDATA="IMAGE" name="gr16"></istex:entity><istex:entity SYSTEM="gr17" NDATA="IMAGE" name="gr17"></istex:entity><istex:entity SYSTEM="gr18" NDATA="IMAGE" name="gr18"></istex:entity><istex:entity SYSTEM="gr19" NDATA="IMAGE" name="gr19"></istex:entity><istex:entity SYSTEM="gr20" NDATA="IMAGE" name="gr20"></istex:entity><istex:entity SYSTEM="gr21" NDATA="IMAGE" name="gr21"></istex:entity><istex:entity SYSTEM="gr22" NDATA="IMAGE" name="gr22"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla" xml:lang="en"><item-info><jid>ECOMOD</jid><aid>2688</aid><ce:pii>S0304-3800(00)00364-1</ce:pii><ce:doi>10.1016/S0304-3800(00)00364-1</ce:doi><ce:copyright type="full-transfer" year="2001">Elsevier Science B.V.</ce:copyright></item-info><head><ce:title>National spatial crop yield simulation using GIS-based crop production model</ce:title><ce:author-group><ce:author><ce:given-name>Satya</ce:given-name><ce:surname>Priya</ce:surname><ce:cross-ref refid="CORR1">*</ce:cross-ref><ce:e-address>satya@skl.iis.u-tokyo.ac.jp</ce:e-address></ce:author><ce:author><ce:given-name>Ryosuke</ce:given-name><ce:surname>Shibasaki</ce:surname><ce:e-address>shiba@skl.iis.u-tokyo.ac.jp</ce:e-address></ce:author><ce:affiliation><ce:textfn>Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan</ce:textfn></ce:affiliation><ce:correspondence id="CORR1"><ce:label>*</ce:label><ce:text>Corresponding author. Tel.: +81-3-54526412; fax: +81-3-54526414</ce:text></ce:correspondence></ce:author-group><ce:date-received day="2" month="11" year="1999"></ce:date-received><ce:date-revised day="6" month="6" year="2000"></ce:date-revised><ce:date-accepted day="14" month="7" year="2000"></ce:date-accepted><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Traditional decision support systems based on crop simulation models are normally site-specific. In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model ‘Spatial EPIC’ was developed based on a crop simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is necessary to feed the fine resolution data at each grid-cell in order to ‘spatialize’ crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data, which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. national scale (approx. 50 km cell-size) and regional scale (approx. 10 km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Crop simulation models</ce:text></ce:keyword><ce:keyword><ce:text>Geographic Information Systems</ce:text></ce:keyword><ce:keyword><ce:text>Agroecosystem</ce:text></ce:keyword><ce:keyword><ce:text>National analysis and planning</ce:text></ce:keyword><ce:keyword><ce:text>Crop productivity</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>National spatial crop yield simulation using GIS-based crop production model</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>National spatial crop yield simulation using GIS-based crop production model</title></titleInfo><name type="personal"><namePart type="given">Satya</namePart><namePart type="family">Priya</namePart><affiliation>Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan</affiliation><affiliation>E-mail: satya@skl.iis.u-tokyo.ac.jp</affiliation><description>Corresponding author. Tel.: +81-3-54526412; fax: +81-3-54526414</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ryosuke</namePart><namePart type="family">Shibasaki</namePart><affiliation>Center for Spatial Information Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan</affiliation><affiliation>E-mail: satya@skl.iis.u-tokyo.ac.jp</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">2001</dateIssued><dateModified encoding="w3cdtf">2000-06-06</dateModified><copyrightDate encoding="w3cdtf">2001</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Traditional decision support systems based on crop simulation models are normally site-specific. In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model ‘Spatial EPIC’ was developed based on a crop simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is necessary to feed the fine resolution data at each grid-cell in order to ‘spatialize’ crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data, which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. national scale (approx. 50 km cell-size) and regional scale (approx. 10 km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account.</abstract><note type="content">Fig. 1: Physical components of the model.</note><note type="content">Fig. 2: Modeling linkage diagram.</note><note type="content">Fig. 3: Brief schematic presentation of modeling under ‘Spatial-EPIC’.</note><note type="content">Fig. 4: Concept of ‘weather’ generator (generating high resolution temporal daily data from coarse resolution monthly data).</note><note type="content">Fig. 5: Concept of ‘slope’ generator (deriving the range and impact of two resolution DEM computed slope over soil loss).</note><note type="content">Fig. 6: Example of data applied in the model.</note><note type="content">Fig. 7: Characterization of fertilizer layer applied: (a) state level; (b) district level in the year 1990.</note><note type="content">Fig. 8: Characterization of simulated root zone soil moisture district level in the year 1990.</note><note type="content">Fig. 9: Main growing region of corn (maize), wheat and rice crop in India.</note><note type="content">Fig. 10: State wise comparison of maize crop yield (t/ha).</note><note type="content">Fig. 11: State wise comparison of wheat crop yield (t/ha).</note><note type="content">Fig. 12: State wise comparison of rice crop yield (t/ha).</note><note type="content">Fig. 13: Rough Spatial validation of maize crop in the year 1990–1991.</note><note type="content">Fig. 14: Rough Spatial validation of wheat crop in the year 1990–1991.</note><note type="content">Fig. 15: Rough Spatial validation of rice crop in the year 1990–1991.</note><note type="content">Fig. 16: Time-series validation of maize crop in Bihar.</note><note type="content">Fig. 17: Time-series validation of rice crop in Bihar.</note><note type="content">Fig. 18: Time-series validation of wheat crop in Bihar.</note><note type="content">Fig. 19: Sensitivity analysis of model response with two different resolutions of soil data over rice crop.</note><note type="content">Fig. 20: Direct impact of input data (soil pH) on crop yield; an example of model sensitivity.</note><note type="content">Fig. 21: Comparision of two different resolutions data impact over wheat yield.</note><note type="content">Fig. 22: Model application as a climate impact assessment over rice crop.</note><note type="content">Table 1: Means and standard deviations of reported and simulated yields</note><subject lang="en"><genre>Keywords</genre><topic>Crop simulation models</topic><topic>Geographic Information Systems</topic><topic>Agroecosystem</topic><topic>National analysis and planning</topic><topic>Crop productivity</topic></subject><relatedItem type="host"><titleInfo><title>Ecological Modelling</title></titleInfo><titleInfo type="abbreviated"><title>ECOMOD</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">20010120</dateIssued></originInfo><identifier type="ISSN">0304-3800</identifier><identifier type="PII">S0304-3800(00)X0111-1</identifier><part><date>20010120</date><detail type="volume"><number>136</number><caption>vol.</caption></detail><detail type="issue"><number>2–3</number><caption>no.</caption></detail><extent unit="issue pages"><start>103</start><end>310</end></extent><extent unit="pages"><start>113</start><end>129</end></extent></part></relatedItem><identifier type="istex">F0529151583246462F9B64855CC85D97D06C54AC</identifier><identifier type="DOI">10.1016/S0304-3800(00)00364-1</identifier><identifier type="PII">S0304-3800(00)00364-1</identifier><accessCondition type="use and reproduction" contentType="copyright">©2001 Elsevier Science B.V.</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science B.V., ©2001</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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